1. Field of the Invention
This invention generally relates to the field of diabetes management tools. More specifically, this invention relates to a system for automated analysis and interpretation of glucose data collected from diabetic patients.
2. Description of the Prior Art
Home glucose monitoring by diabetics is becoming increasingly routine in modern-day diabetes management. Patients are typically required to maintain hand-written paper log books for manually recording glucose readings and other relevant information. More specifically, patients now measure their blood glucose at scheduled times, possibly to determine the amount of insulin based on the current blood glucose result, and record this information in a personal log book.
Physicians are subsequently faced with the task of carefully reviewing the hand-recorded data for use in optimizing the patient's diabetes therapy. In order to make intelligent and meaningful decisions regarding therapeutic modifications, it becomes necessary for the examining physician to not only summarize the available information but, more importantly, to analyze hundreds of time-dependant observations collected over an extended period of time in order to spot unusual and clinically significant features requiring any modifications of the patient's current diabetes management schedule. The recorded data typically extends over a period of time spanning several weeks or months and constitutes a vast amount of time-dependant data. As an example, a patient on a regimen of three injections of mixed insulin per day who records only the most basic diabetes management data, i.e., only insulin and glucose levels, will generate a personal log comprising 810 data items over a three month period. It is extremely difficult, if not impossible, for a physician to be able to review and assimilate all the clinical and therapeutic implications of this vast amount of data in any reasonable amount of time.
While the introduction of glucose meters with various memory functions has greatly simplified the data recording process and increased the reliability of stored data, the large amounts of recorded data have made the interpretation task complicated. Such glucose meters now make it possible for patients to maintain a scrupulous record of glucose readings and insulin dosage taken over a lengthy period of time. More importantly, it is also possible with present-day devices for patients to record other clinically relevant data such as diet and exercise factors, and life-style information. All such stored data can conveniently be transferred to a physician's office, preferably via a communications link such as an acoustic modem line, where it can be reviewed in printed or video display format for making appropriate treatment recommendations.
The vast amount of stored glucose monitoring-related data has tremendously complicated the physician's ability to effectively study data corresponding to hundreds of time-dependant observations and measurements in order to focus on key clinical implications buried therein and generate meaningful and intelligent diabetes treatment decisions. Accordingly, computer-based methods must be adopted for efficiently tackling the high volume of monitored data and the complexity of the data interpretation task.
Attempts have been made at computerized automation of the data interpretation task and personal computer programs have been developed for interactive display of diabetes patient data and creation of paper reports therefrom. Such programs are typically menu-driven microcomputer programs which are adapted to process pre-recorded diabetes patient data in order to generate a statistical and graphical analysis of the data. Although the processing of voluminous diabetes patient data and the generation of associated graphs and statistical analyses does assist the physician in his review, it still becomes incumbent on the physician to spend a significant amount of time interactively guiding the analysis, studying the generated program results and performing additional synthesis of the data in order to detect clinical implications contained therein.
In essence, traditional approaches to automated analysis of diabetes data provide a relatively superficial analysis and an assortment of graphical displays based upon certain predefined statistical calculations. However, the time-consuming and complicated synthesis and interpretation of clinical implications associated with the processed data still need to be performed by the reviewing physician, and significant interaction is still required on behalf of the physician.